(this document is updated as the weeks proceed)
Week 7: Stats and studies: Correlation and causation
Week 8
Controlling for in regression and multicollinearity
Week 9
We’re going to go back and forth between bare-bones examples and arguments from the wild, giving you more and more tools to deal with the real-world ones.
Week 11
Casting an argument
Fallacies.
Casting the we must bomb Iran argument
Week 12
Analysing scientific abstracts
Mindset again - System 1 vs. System 2 reading
Slippery slope
Analysing the Molly and Bea argument
Week 13
Don’t leave your shoes in the middle of the floor
You always leave your shoes in the middle of the floor
Daniel Pink on How to Persuade Others with the Right Questions
This guy
I’m not one of those people who went into psychology because they wanted to deal with feelings
What I’m into is evidence, reasons and logic. But I’ve learned I can only have those conversations with certain people if I deal with their feelings.
Base rates, probabilities, and correlations
Is there a correlation between being male and liking avocados?
Suppose we learn that most males like avocados and also that most people who like avocadoes are male. Can we conclude that liking avocadoes is correlated with being male? It’s tempting to think the answer is “yes.” Understanding why that’s the wrong answer is crucial to having a full understanding of correlation.
For two dichomotous variables like male/female and liking avocados versus not, a positive correlation would mean that a higher proportion of males like avocado than females.
We know two things * Most males like avocado * Most people who like avocado are male
To establish a correlation, what we need to know is whether males like avocados at a higher rate than females do. But that simply does not follow from the fact that most males like avocados and most who like avocado are male. Look at this example:
| Like Avocado | Male | Female |
|---|---|---|
| No | 40 | 38 |
| Yes | 60 | 58 |
Most males in this example
What proportion of males like avocados? What proportion of females like avocados
Most people in the world are male (by a small amount). So we should expect most avocado eaters to be male even if males and females like avocados at the same rate.
Putting these two facts together still doesn’t give us a correlation, because they could both be true even if males and females own cell phones at the same rate. - Make a 2 x 2 table. For base rate of each - being male and owning a cellphone.
Most northern Hemisphere residents have above world average income. Most people with above world average income are in the northern hemisphere.
Most N. Hemisphere countries have more than 300 COVID-19 deaths. Most countries with more than 300 COVID-19 deaths are in the Northern Hemisphere. Is there a correlation between being in the N. Hemisphere and having more than 300 COVID-19 deaths? Maybe not, because most countries are in the N. Hemisphere anyway, so the second statement doesn’t tell us much. We need more to know whether the proportion of northern hemisphere countries with >300 COVID-19 deaths is greater than the proportion of southern hemisphere countries.
- Another kind of mistake is simply that we fail to think proportionally. For example, suppose we've only observed John when it's cold and we notice that he has worn a hat 70% of the time. Can we conclude that there is a correlation in our observations between his wearing a hat and cold temperatures? Of course not! What if he wears a hat 70% of the time regardless of the temperature? In that case, there is no special correlation between his hat wearing and the cold: he just loves wearing hats.
If we are told, "Most of the time when it's cold, John wears a hat," it's easy to forget that this is not enough to establish a correlation. To infer a correlation, we have to assume that John does not wear a hat most of the time on other days too . Maybe this is a safe assumption to make, but maybe not. The point is that if we just ignore it, we are neglecting the base rate, a mistake we encountered in the previous chapter.
those really were the droids you were looking for
- This stormtrooper has realized that he made a big mistake. That’s good because then he can learn from the mistake.
Related to not wanting to admit that one is wrong.
divorced
This guy
- If one doesn’t know one made an error, one won’t learn much from the error. And wrong theories of the world never get fixed!
Prior to the 2012 election, average person said likelihood of Obama winning was 59%.
After the election, the average person (different set of people) said 68% (p < .001).
Hindsight bias
Pundits carry on thinking all their political theories are correct.
Kahan has shown that evoking curiousity can help. Use it on yourself, too - I’m curious why you feel that way.
Survivorship * Bruce’s slide #132 * Famous people are usually very good, but also very lucky * “Failure to look for what is missing is a common shortcoming” https://youarenotsosmart.com/2013/05/23/survivorship-bias/ covers Wald, * Heuristic: Chesterton’s fence, as selection bias?